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<a href="_svm_logistic_interpretation_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       Maximum-likelihood model selection for binary support vector machines.</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * </span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * \author      M.Tuma, T.Glasmachers</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \date        2009-2012</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> *</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * </span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * </span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> *</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> */</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#ifndef SHARK_ML_SVMLOGISTICINTERPRETATION_H</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#define SHARK_ML_SVMLOGISTICINTERPRETATION_H</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="_c_v_dataset_tools_8h.html">shark/Data/CVDatasetTools.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_c_svm_derivative_8h.html">shark/Models/Kernels/CSvmDerivative.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#include &lt;<a class="code" href="_c_svm_trainer_8h.html">shark/Algorithms/Trainers/CSvmTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_objective_function_8h.html" title="AbstractObjectiveFunction.">shark/ObjectiveFunctions/AbstractObjectiveFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_error_function_8h.html">shark/ObjectiveFunctions/ErrorFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_cross_entropy_8h.html">shark/ObjectiveFunctions/Loss/CrossEntropy.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_b_f_g_s_8h.html">shark/Algorithms/GradientDescent/BFGS.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment"></span> </div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// \brief Maximum-likelihood model selection score for binary support vector machines</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">///</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// This class implements the maximum-likelihood based SVM model selection</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// procedure presented in the article &quot;Glasmachers and C. Igel. Maximum</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// Parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.&quot;</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// At this point, only binary C-SVMs are supported.</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// This class implements an AbstactObjectiveFunction. In detail, it provides</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// a differentiable measure of how well a C-SVM with given hyperparameters fulfills</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// the maximum-likelihood score presented in the paper. This error measure can then</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// be optimized for externally via gradient-based optimizers. In other words, this</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// class provides a score, not an optimization method or a training algorithm. The</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// C-SVM parameters have to be optimized with regard to this measure</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// \ingroup kerneloptimization</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType = RealVector&gt;</div>
<div class="foldopen" id="foldopen00062" data-start="{" data-end="};">
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html">   62</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_svm_logistic_interpretation.html" title="Maximum-likelihood model selection score for binary support vector machines.">SvmLogisticInterpretation</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html" title="Super class of all objective functions for optimization and learning.">AbstractObjectiveFunction</a>&lt; RealVector, double &gt; {</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#aacebd95928685e014eafb5fb6efe20b3">   64</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_c_v_folds.html">CVFolds&lt; LabeledData&lt;InputType, unsigned int&gt;</a> &gt; <a class="code hl_typedef" href="classshark_1_1_svm_logistic_interpretation.html#aacebd95928685e014eafb5fb6efe20b3">FoldsType</a>;</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a27b2b67af5e5b9d2de969f43320cbca4">   65</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_svm_logistic_interpretation.html#a27b2b67af5e5b9d2de969f43320cbca4">KernelType</a>;</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a0d205e3f7137f8535c650637b4f6f46b">   67</a></span>    <a class="code hl_class" href="classshark_1_1_c_v_folds.html">FoldsType</a> <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a0d205e3f7137f8535c650637b4f6f46b" title="the underlying partitioned dataset.">m_folds</a>;          <span class="comment">///&lt; the underlying partitioned dataset.</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b">   68</a></span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a> *<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b" title="the kernel with which to run the SVM">mep_kernel</a>;     <span class="comment">///&lt; the kernel with which to run the SVM</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9">   69</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a>;         <span class="comment">///&lt; for convenience, the Number of Hyper Parameters</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04">   70</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>;         <span class="comment">///&lt; for convenience, the Number of Kernel Parameters</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a9a17b63f0101e55aa0e3249cfc253f8c">   71</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a9a17b63f0101e55aa0e3249cfc253f8c" title="the number of folds to be used in cross-validation">m_numFolds</a>;    <span class="comment">///&lt; the number of folds to be used in cross-validation</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22">   72</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22" title="overall number of samples in the dataset">m_numSamples</a>;  <span class="comment">///&lt; overall number of samples in the dataset</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a03027cc334469ae03a79f47fe428012a">   73</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a03027cc334469ae03a79f47fe428012a" title="input dimensionality">m_inputDims</a>;   <span class="comment">///&lt; input dimensionality</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6">   74</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a>; <span class="comment">///&lt; the SVM regularization parameter C is passed for unconstrained optimization, and the derivative should compensate for that</span></div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a19565575b801e17ebdf3597bc49bd526">   75</a></span>    <a class="code hl_struct" href="structshark_1_1_qp_stopping_condition.html" title="stopping conditions for quadratic programming">QpStoppingCondition</a> *<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a19565575b801e17ebdf3597bc49bd526" title="the stopping criterion that is to be passed to the SVM trainer.">mep_svmStoppingCondition</a>; <span class="comment">///&lt; the stopping criterion that is to be passed to the SVM trainer.</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment"></span> </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    //! constructor.</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">    //! \param folds an already partitioned dataset (i.e., a CVFolds object)</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">    //! \param kernel pointer to the kernel to be used within the SVMs.</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    //! \param unconstrained whether or not the C-parameter of/for the C-SVM is passed for unconstrained optimization mode.</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">    //! \param stop_cond the stopping conditions which are to be passed to the</span></div>
<div class="foldopen" id="foldopen00083" data-start="{" data-end="}">
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#acd0536c312b58338f044a8d655d8739a">   83</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_svm_logistic_interpretation.html#acd0536c312b58338f044a8d655d8739a">SvmLogisticInterpretation</a>(</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <a class="code hl_class" href="classshark_1_1_c_v_folds.html">FoldsType</a> <span class="keyword">const</span> &amp;folds, <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a> *kernel,</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            <span class="keywordtype">bool</span> unconstrained = <span class="keyword">true</span>, <a class="code hl_struct" href="structshark_1_1_qp_stopping_condition.html" title="stopping conditions for quadratic programming">QpStoppingCondition</a> *stop_cond = NULL</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    )</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    : <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b" title="the kernel with which to run the SVM">mep_kernel</a>(kernel)</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>    ,  <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a>(kernel-&gt;parameterVector().size()+1)</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    ,  <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>(kernel-&gt;parameterVector().size())</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    ,  <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a9a17b63f0101e55aa0e3249cfc253f8c" title="the number of folds to be used in cross-validation">m_numFolds</a>(folds.size())  <span class="comment">//gets number of folds!</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    ,  <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22" title="overall number of samples in the dataset">m_numSamples</a>(folds.dataset().numberOfElements())</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    ,  <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a03027cc334469ae03a79f47fe428012a" title="input dimensionality">m_inputDims</a>(<a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(folds.dataset()))</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    ,  <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a>(unconstrained)</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    ,  <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a19565575b801e17ebdf3597bc49bd526" title="the stopping criterion that is to be passed to the SVM trainer.">mep_svmStoppingCondition</a>(stop_cond)</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    {</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(kernel != NULL, <span class="stringliteral">&quot;[SvmLogisticInterpretation::SvmLogisticInterpretation] kernel is not allowed to be NULL&quot;</span>);  <span class="comment">//mtq: necessary despite indirect check via call in initialization list?</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a9a17b63f0101e55aa0e3249cfc253f8c" title="the number of folds to be used in cross-validation">m_numFolds</a> &gt; 1, <span class="stringliteral">&quot;[SvmLogisticInterpretation::SvmLogisticInterpretation] please provide a meaningful number of folds for cross validation&quot;</span>);</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        <span class="keywordflow">if</span> (!<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a>)   <span class="comment">//mtq: important: we additionally need to deal with kernel feasibility indicators! important!</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>            <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aa0574c1ccb7c3791cc09bae4a7198429a" title="The objective function is constrained.">IS_CONSTRAINED_FEATURE</a>;</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aad3475b458576c8760f28d8d81f4eda86" title="The function can be evaluated and evalDerivative returns a meaningless value (for example std::numeri...">HAS_VALUE</a>;</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b" title="the kernel with which to run the SVM">mep_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#ac0c799ac75db64200256ed50d34d2411">hasFirstParameterDerivative</a>())</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>            <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aa0bc7673a369df5f86ddd6ba6735f4971" title="The method evalDerivative is implemented for the first derivative and returns a sensible value.">HAS_FIRST_DERIVATIVE</a>;</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a0d205e3f7137f8535c650637b4f6f46b" title="the underlying partitioned dataset.">m_folds</a> = folds;</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    }</div>
</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment"></span> </div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00107" data-start="{" data-end="}">
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a609bdc62676220fa7b1e27366db2e0fd">  107</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_svm_logistic_interpretation.html#a609bdc62676220fa7b1e27366db2e0fd" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;SvmLogisticInterpretation&quot;</span>; }</div>
</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment"></span> </div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    //! checks whether the search point provided is feasible</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    //! \param input the point to test for feasibility</span></div>
<div class="foldopen" id="foldopen00112" data-start="{" data-end="}">
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a9a2421271fa2504061af8532340529c6">  112</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_svm_logistic_interpretation.html#a9a2421271fa2504061af8532340529c6">isFeasible</a>(<span class="keyword">const</span> <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> &amp;input)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(input.size() == <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a>);</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <span class="keywordflow">if</span> (input(0) &lt;= 0.0 &amp;&amp; !<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a>) {</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        }</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    }</div>
</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span> </div>
<div class="foldopen" id="foldopen00120" data-start="{" data-end="}">
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#ae710c2360a5059c8f872757bdcb4c631">  120</a></span>    std::size_t <a class="code hl_function" href="classshark_1_1_svm_logistic_interpretation.html#ae710c2360a5059c8f872757bdcb4c631" title="Accesses the number of variables.">numberOfVariables</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a>;</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    }</div>
</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment"></span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    //! train a number of SVMs in a cross-validation setting using the hyperparameters passed to this method.</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">    //! the output scores from all validations sets are then concatenated. together with the true labels, these</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment">    //! scores can then be used to fit a sigmoid such that it becomes as good as possible a model for the</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment">    //! class membership probabilities given the SVM output scores. This method returns the negative likelihood</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    //! of the best fitting sigmoid, given a set of SVM hyperparameters.</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    //! \param parameters the SVM hyperparameters to use for all C-SVMs</span></div>
<div class="foldopen" id="foldopen00130" data-start="{" data-end="}">
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#a67444886b71f0bef297aaa3d396e6b81">  130</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_svm_logistic_interpretation.html#a67444886b71f0bef297aaa3d396e6b81">eval</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <span class="keyword">const</span> &amp;parameters)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a> == parameters.size(), <span class="stringliteral">&quot;[SvmLogisticInterpretation::eval] wrong number of parameters&quot;</span>);</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <span class="comment">// initialize, copy parameters</span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <span class="keywordtype">double</span> C_reg = (<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a> ? std::exp(parameters(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>)) : parameters(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>));   <span class="comment">//set up regularization parameter</span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b" title="the kernel with which to run the SVM">mep_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(subrange(parameters, 0, <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>));   <span class="comment">//set up kernel parameters</span></div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="comment">// Stores the stacked CV predictions for every fold.</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> validation_dataset;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="comment">// for each fold, train an svm and get predictions on the validation data</span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a9a17b63f0101e55aa0e3249cfc253f8c" title="the number of folds to be used in cross-validation">m_numFolds</a>; i++) {</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            <span class="comment">// init SVM</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>            <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a> svm;</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            <a class="code hl_class" href="classshark_1_1_c_svm_trainer.html" title="Training of C-SVMs for binary classification.">CSvmTrainer&lt;InputType, double&gt;</a> csvm_trainer(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b" title="the kernel with which to run the SVM">mep_kernel</a>, C_reg, <span class="keyword">true</span>, <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a>);   <span class="comment">//the trainer</span></div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>            csvm_trainer.<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">sparsify</a>() = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>            <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a19565575b801e17ebdf3597bc49bd526" title="the stopping criterion that is to be passed to the SVM trainer.">mep_svmStoppingCondition</a> != NULL) {</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>                csvm_trainer.<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">stoppingCondition</a>() = *<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a19565575b801e17ebdf3597bc49bd526" title="the stopping criterion that is to be passed to the SVM trainer.">mep_svmStoppingCondition</a>;</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            }</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span> </div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>            <span class="comment">// train SVM on current training fold</span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>            csvm_trainer.<a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a9e801518bfba9d02e0749181a5deb0fc" title="Train the C-SVM.">train</a>(svm, <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a0d205e3f7137f8535c650637b4f6f46b" title="the underlying partitioned dataset.">m_folds</a>.<a class="code hl_function" href="classshark_1_1_c_v_folds.html#a71a49586552161e0027348fa3a165310">training</a>(i));</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>            </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>            <span class="comment">//append validation predictions</span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            validation_dataset.<a class="code hl_function" href="group__shark__globals.html#ga3c9be5eff818d2c5eb10b35f7b47ee14" title="Appends the contents of another data object to the end.">append</a>(<a class="code hl_function" href="group__shark__globals.html#gaf650c7559860ceb0d6b5e3ef3a1be1f3" title="Transforms the inputs of a dataset and return the transformed result.">transformInputs</a>(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a0d205e3f7137f8535c650637b4f6f46b" title="the underlying partitioned dataset.">m_folds</a>.<a class="code hl_function" href="classshark_1_1_c_v_folds.html#a02f53dc5f3585ac17b190bbbe9549b88">validation</a>(i),svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>()));</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        }</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span> </div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <span class="comment">// Fit a logistic regression to the prediction</span></div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        <a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;&gt;</a> logistic_model = fitLogistic(validation_dataset);</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="comment">//to evaluate, we use cross entropy loss on the fitted model </span></div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        <a class="code hl_class" href="classshark_1_1_cross_entropy.html" title="Error measure for classification tasks that can be used as the objective function for training.">CrossEntropy&lt;unsigned int, RealVector&gt;</a> logistic_loss;</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">return</span> logistic_loss(validation_dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(),logistic_model(validation_dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>()));</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>    }</div>
</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span><span class="comment"></span> </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span><span class="comment">    //! the derivative of the error() function above w.r.t. the parameters.</span></div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span><span class="comment">    //! \param parameters the SVM hyperparameters to use for all C-SVMs</span></div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="comment">    //! \param derivative will store the computed derivative w.r.t. the current hyperparameters</span></div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment"></span>    <span class="comment">// mtq: should this also follow the first-call-error()-then-call-deriv() paradigm?</span></div>
<div class="foldopen" id="foldopen00166" data-start="{" data-end="}">
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"><a class="line" href="classshark_1_1_svm_logistic_interpretation.html#aaf23373024f5c16cb6de60ee2c4fc2c8">  166</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_svm_logistic_interpretation.html#aaf23373024f5c16cb6de60ee2c4fc2c8">evalDerivative</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <span class="keyword">const</span> &amp;parameters, <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a29804371954a360f09696adea7cfd839">FirstOrderDerivative</a> &amp;derivative)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a> == parameters.size(), <span class="stringliteral">&quot;[SvmLogisticInterpretation::evalDerivative] wrong number of parameters&quot;</span>);</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        <span class="comment">// initialize, copy parameters</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        <span class="keywordtype">double</span> C_reg = (<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a> ? std::exp(parameters(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>)) : parameters(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>));   <span class="comment">//set up regularization parameter</span></div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b" title="the kernel with which to run the SVM">mep_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(subrange(parameters, 0, <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a1c61672212c6e54cb1178dd5bc859c04" title="for convenience, the Number of Kernel Parameters">m_nkp</a>));   <span class="comment">//set up kernel parameters</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        <span class="comment">// these two will be filled in order corresp. to all CV validation partitions stacked</span></div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        <span class="comment">// behind one another, and then used to create datasets with</span></div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        std::vector&lt; unsigned int &gt; tmp_helper_labels(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22" title="overall number of samples in the dataset">m_numSamples</a>);</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        std::vector&lt; RealVector &gt; tmp_helper_preds(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22" title="overall number of samples in the dataset">m_numSamples</a>);</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span> </div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> next_label = 0; <span class="comment">//helper index counter to monitor the next position to be filled in the above vectors</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        <span class="comment">// init variables especially for derivative</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        RealMatrix all_validation_predict_derivs(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22" title="overall number of samples in the dataset">m_numSamples</a>, <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a>);   <span class="comment">//will hold derivatives of all output scores w.r.t. all hyperparameters</span></div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        RealVector der; <span class="comment">//temporary helper for derivative calls</span></div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span> </div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        <span class="comment">// for each fold, train an svm and get predictions on the validation data</span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a9a17b63f0101e55aa0e3249cfc253f8c" title="the number of folds to be used in cross-validation">m_numFolds</a>; i++) {</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>            <span class="comment">// get current train/validation partitions as well as corresponding labels</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> cur_train_data = <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a0d205e3f7137f8535c650637b4f6f46b" title="the underlying partitioned dataset.">m_folds</a>.<a class="code hl_function" href="classshark_1_1_c_v_folds.html#a71a49586552161e0027348fa3a165310">training</a>(i);</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> cur_valid_data = <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a0d205e3f7137f8535c650637b4f6f46b" title="the underlying partitioned dataset.">m_folds</a>.<a class="code hl_function" href="classshark_1_1_c_v_folds.html#a02f53dc5f3585ac17b190bbbe9549b88">validation</a>(i);</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            std::size_t cur_vsize = cur_valid_data.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            <a class="code hl_class" href="classshark_1_1_data.html">Data&lt; unsigned int &gt;</a> cur_vlabels = cur_valid_data.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(); <span class="comment">//validation labels of this fold</span></div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>            <a class="code hl_class" href="classshark_1_1_data.html">Data&lt; RealVector &gt;</a> cur_vinputs = cur_valid_data.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(); <span class="comment">//validation inputs of this fold</span></div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>            <a class="code hl_class" href="classshark_1_1_data.html">Data&lt; RealVector &gt;</a> cur_vscores; <span class="comment">//will hold SVM output scores for current validation partition</span></div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>            <span class="comment">// init SVM</span></div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>            <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a> svm;   <span class="comment">//the SVM</span></div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>            <a class="code hl_class" href="classshark_1_1_c_svm_trainer.html" title="Training of C-SVMs for binary classification.">CSvmTrainer&lt;InputType, double&gt;</a> csvm_trainer(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#ae6a2b133a65cdb723b3a5fe4b71b961b" title="the kernel with which to run the SVM">mep_kernel</a>, C_reg, <span class="keyword">true</span>, <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a7c5a5bb5f1a377b3133a9598e6e18af6" title="the SVM regularization parameter C is passed for unconstrained optimization, and the derivative shoul...">m_svmCIsUnconstrained</a>);   <span class="comment">//the trainer</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>            csvm_trainer.<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">sparsify</a>() = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>            csvm_trainer.<a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a7fdb7e988fa0949ca5e96faf9c7bcf48">setComputeBinaryDerivative</a>(<span class="keyword">true</span>);</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>            <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a19565575b801e17ebdf3597bc49bd526" title="the stopping criterion that is to be passed to the SVM trainer.">mep_svmStoppingCondition</a> != NULL) {</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>                csvm_trainer.<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">stoppingCondition</a>() = *<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a19565575b801e17ebdf3597bc49bd526" title="the stopping criterion that is to be passed to the SVM trainer.">mep_svmStoppingCondition</a>;</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>            }</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>            <span class="comment">// train SVM on current fold</span></div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>            csvm_trainer.<a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a9e801518bfba9d02e0749181a5deb0fc" title="Train the C-SVM.">train</a>(svm, cur_train_data);</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>            <a class="code hl_class" href="classshark_1_1_c_svm_derivative.html" title="This class provides two main member functions for computing the derivative of a C-SVM hypothesis w....">CSvmDerivative&lt;InputType&gt;</a> svm_deriv(&amp;svm, &amp;csvm_trainer);</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>            cur_vscores = svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>()(cur_valid_data.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>());   <span class="comment">//will result in a dataset of RealVector as output</span></div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>            <span class="comment">// copy the scores and corresponding labels to the dataset-wide storage</span></div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>            <span class="keywordflow">for</span> (std::size_t j=0; j&lt;cur_vsize; j++) {</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>                <span class="comment">// copy label and prediction score</span></div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>                tmp_helper_labels[next_label] = cur_vlabels.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(j);</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>                tmp_helper_preds[next_label] = cur_vscores.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(j);</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>                <span class="comment">// get and store the derivative of the score w.r.t. the hyperparameters</span></div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>                svm_deriv.<a class="code hl_function" href="classshark_1_1_c_svm_derivative.html#a2e46e908d670b88e6e5ab32f3482e7d8">modelCSvmParameterDerivative</a>(cur_vinputs.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(j), der);</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>                noalias(row(all_validation_predict_derivs, next_label)) = der;   <span class="comment">//fast assignment of the derivative to the correct matrix row</span></div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>                ++next_label;</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>            }</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        }</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        </div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <span class="comment">// now we got it all: the predictions across the validation folds, plus the correct corresponding</span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <span class="comment">// labels. so we go ahead and fit a logistic regression</span></div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> validation_dataset= <a class="code hl_function" href="group__shark__globals.html#ga409b50a287df842bd49e7434a8bbf69e" title="creates a labeled data object from two ranges, representing inputs and labels">createLabeledDataFromRange</a>(tmp_helper_preds, tmp_helper_labels);</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        <a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;&gt;</a> logistic_model = fitLogistic(validation_dataset);</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        </div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        <span class="comment">// to evaluate, we use cross entropy loss on the fitted model  and compute </span></div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        <span class="comment">// the derivative wrt the svm model parameters.</span></div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        derivative.resize(<a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a02e5bdf24c4bfeca6f92fd38a4270ed9" title="for convenience, the Number of Hyper Parameters">m_nhp</a>);</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>        derivative.clear();</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>        <span class="keywordtype">double</span> error = 0;</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>        std::size_t start = 0;</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>        <span class="keywordflow">for</span>(<span class="keyword">auto</span> <span class="keyword">const</span>&amp; batch: validation_dataset.<a class="code hl_function" href="group__shark__globals.html#ga6c3b7d09e870412534ef27988b950fc6" title="Returns the range of batches.">batches</a>()){</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>            std::size_t end = start+batch.size();</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>            <a class="code hl_class" href="classshark_1_1_cross_entropy.html" title="Error measure for classification tasks that can be used as the objective function for training.">CrossEntropy&lt;unsigned int, RealVector&gt;</a> logistic_loss;</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>            RealMatrix lossGradient;</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>            error += logistic_loss.evalDerivative(batch.label,logistic_model(batch.input),lossGradient);</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>            noalias(derivative) += column(lossGradient,0) % rows(all_validation_predict_derivs,start,end);</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>            start = end;</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        }</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>        derivative *= logistic_model.<a class="code hl_function" href="classshark_1_1_linear_model.html#a26fc78bc3f8a04e11b41542c3dfe3dec" title="obtain the parameter vector">parameterVector</a>()(0);</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>        derivative /= <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22" title="overall number of samples in the dataset">m_numSamples</a>;</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>        <span class="keywordflow">return</span> error / <a class="code hl_variable" href="classshark_1_1_svm_logistic_interpretation.html#a370c5373314e75218c5802779378ac22" title="overall number of samples in the dataset">m_numSamples</a>;</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>    }</div>
</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    <a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;&gt;</a> fitLogistic(<a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> <span class="keyword">const</span>&amp; data)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>        <a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;&gt;</a> logistic_model;</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>        logistic_model.<a class="code hl_function" href="classshark_1_1_linear_model.html#a901efd377ffaf2d09a50d2adcbd6f9d4" title="overwrite structure and parameters">setStructure</a>(1,1, <span class="keyword">true</span>);<span class="comment">//1 input, 1 output, bias = 2 parameters</span></div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        <a class="code hl_class" href="classshark_1_1_cross_entropy.html" title="Error measure for classification tasks that can be used as the objective function for training.">CrossEntropy&lt;unsigned int, RealVector&gt;</a> logistic_loss;</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <a class="code hl_class" href="classshark_1_1_error_function.html" title="Objective function for supervised learning.">ErrorFunction&lt;&gt;</a> error(data, &amp;logistic_model, &amp; logistic_loss);</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        <a class="code hl_class" href="classshark_1_1_b_f_g_s.html" title="Broyden, Fletcher, Goldfarb, Shannon algorithm for unconstraint optimization.">BFGS&lt;&gt;</a> optimizer;</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        optimizer.<a class="code hl_function" href="classshark_1_1_abstract_line_search_optimizer.html#a197982cec7de486f937715a3f280be72">init</a>(error);</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>        <span class="comment">//this converges after very few iterations (typically 20 function evaluations)</span></div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        <span class="keywordflow">while</span>(norm_2(optimizer.<a class="code hl_function" href="classshark_1_1_abstract_line_search_optimizer.html#ad35111bf627c76f3cedd4dd5f92fcd9b" title="Returns the derivative at the current point. Can be used for stopping criteria.">derivative</a>())&gt; 1.e-8){</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>            <span class="keywordtype">double</span> lastValue = optimizer.<a class="code hl_function" href="classshark_1_1_abstract_single_objective_optimizer.html#a0909596fcc4f80a8d108859b20b64a81" title="returns the current solution of the optimizer">solution</a>().value;</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            optimizer.<a class="code hl_function" href="classshark_1_1_abstract_line_search_optimizer.html#ae6689563bafd7dbbb02299e161238b26">step</a>(error);</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>            <span class="keywordflow">if</span>(lastValue == optimizer.<a class="code hl_function" href="classshark_1_1_abstract_single_objective_optimizer.html#a0909596fcc4f80a8d108859b20b64a81" title="returns the current solution of the optimizer">solution</a>().value) <span class="keywordflow">break</span>;<span class="comment">//we are done due to numerical precision</span></div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        }</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        logistic_model.<a class="code hl_function" href="classshark_1_1_linear_model.html#ad7074a494d2ac2bc1bc78eb9fd2d5927" title="overwrite the parameter vector">setParameterVector</a>(optimizer.<a class="code hl_function" href="classshark_1_1_abstract_single_objective_optimizer.html#a0909596fcc4f80a8d108859b20b64a81" title="returns the current solution of the optimizer">solution</a>().point);</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        <span class="keywordflow">return</span> logistic_model;</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>    }</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>};</div>
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<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span> </div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span> </div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>}</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span><span class="preprocessor">#endif</span></div>
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